Overview

Dataset statistics

Number of variables11
Number of observations660
Missing cells10
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory56.8 KiB
Average record size in memory88.2 B

Variable types

Numeric10
Categorical1

Alerts

variety has a high cardinality: 242 distinct values High cardinality
dm is highly correlated with ndfdm and 3 other fieldsHigh correlation
ndfdm is highly correlated with dm and 4 other fieldsHigh correlation
adfdm is highly correlated with dm and 4 other fieldsHigh correlation
adldm is highly correlated with ndfdm and 3 other fieldsHigh correlation
me is highly correlated with dm and 4 other fieldsHigh correlation
ivomd is highly correlated with dm and 4 other fieldsHigh correlation
GH > 3 is highly correlated with NH > 1High correlation
NH > 1 is highly correlated with GH > 3High correlation
dm is highly correlated with ndfdm and 3 other fieldsHigh correlation
ash is highly correlated with GH > 3 and 1 other fieldsHigh correlation
ndfdm is highly correlated with dm and 3 other fieldsHigh correlation
adfdm is highly correlated with dm and 4 other fieldsHigh correlation
adldm is highly correlated with adfdm and 2 other fieldsHigh correlation
me is highly correlated with dm and 4 other fieldsHigh correlation
ivomd is highly correlated with dm and 4 other fieldsHigh correlation
GH > 3 is highly correlated with ash and 1 other fieldsHigh correlation
NH > 1 is highly correlated with ash and 1 other fieldsHigh correlation
dm is highly correlated with ndfdm and 3 other fieldsHigh correlation
ndfdm is highly correlated with dm and 3 other fieldsHigh correlation
adfdm is highly correlated with dm and 4 other fieldsHigh correlation
adldm is highly correlated with adfdm and 2 other fieldsHigh correlation
me is highly correlated with dm and 4 other fieldsHigh correlation
ivomd is highly correlated with dm and 4 other fieldsHigh correlation
GH > 3 is highly correlated with NH > 1High correlation
NH > 1 is highly correlated with GH > 3High correlation
dm is highly correlated with ndfdm and 3 other fieldsHigh correlation
ash is highly correlated with ndm and 3 other fieldsHigh correlation
ndm is highly correlated with ash and 4 other fieldsHigh correlation
ndfdm is highly correlated with dm and 4 other fieldsHigh correlation
adfdm is highly correlated with dm and 7 other fieldsHigh correlation
adldm is highly correlated with ndfdm and 4 other fieldsHigh correlation
me is highly correlated with dm and 5 other fieldsHigh correlation
ivomd is highly correlated with dm and 4 other fieldsHigh correlation
GH > 3 is highly correlated with ash and 3 other fieldsHigh correlation
NH > 1 is highly correlated with ash and 3 other fieldsHigh correlation

Reproduction

Analysis started2022-02-24 14:57:26.881262
Analysis finished2022-02-24 14:57:37.734441
Duration10.85 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

dm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct658
Distinct (%)99.8%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean93.42557897
Minimum90.9349213
Maximum95.4276657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-02-24T14:57:37.808281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum90.9349213
5-th percentile92.07533414
Q192.86429215
median93.472641
Q394.02219005
95-th percentile94.62653125
Maximum95.4276657
Range4.4927444
Interquartile range (IQR)1.1578979

Descriptive statistics

Standard deviation0.8021531879
Coefficient of variation (CV)0.00858601249
Kurtosis-0.3323392969
Mean93.42557897
Median Absolute Deviation (MAD)0.5703201
Skewness-0.2322160949
Sum61567.45654
Variance0.6434497369
MonotonicityNot monotonic
2022-02-24T14:57:37.909977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92.64863592
 
0.3%
93.10157011
 
0.2%
92.97486111
 
0.2%
93.05456541
 
0.2%
92.54888151
 
0.2%
93.4975511
 
0.2%
93.36044311
 
0.2%
93.64864351
 
0.2%
93.06878661
 
0.2%
93.75733951
 
0.2%
Other values (648)648
98.2%
ValueCountFrequency (%)
90.93492131
0.2%
91.24831391
0.2%
91.29821011
0.2%
91.32882691
0.2%
91.35836031
0.2%
91.40799711
0.2%
91.4688951
0.2%
91.46929931
0.2%
91.53642271
0.2%
91.55272671
0.2%
ValueCountFrequency (%)
95.42766571
0.2%
95.35942841
0.2%
95.24546811
0.2%
95.2128221
0.2%
95.18978881
0.2%
95.18482211
0.2%
95.16452031
0.2%
95.13468171
0.2%
95.05169681
0.2%
95.01615911
0.2%

ash
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct659
Distinct (%)100.0%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5.402354481
Minimum0.1298218
Maximum27.6425934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-02-24T14:57:38.010743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1298218
5-th percentile2.84676721
Q14.15133
median4.9840698
Q36.0437307
95-th percentile9.27298646
Maximum27.6425934
Range27.5127716
Interquartile range (IQR)1.8924007

Descriptive statistics

Standard deviation2.361642082
Coefficient of variation (CV)0.437150522
Kurtosis20.16867807
Mean5.402354481
Median Absolute Deviation (MAD)0.9364433
Skewness3.280917602
Sum3560.151603
Variance5.577353322
MonotonicityNot monotonic
2022-02-24T14:57:38.105082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.70313071
 
0.2%
3.36442951
 
0.2%
5.6760961
 
0.2%
3.91835021
 
0.2%
3.94333651
 
0.2%
5.37457281
 
0.2%
6.05113221
 
0.2%
15.88843731
 
0.2%
5.18987081
 
0.2%
7.16289141
 
0.2%
Other values (649)649
98.3%
ValueCountFrequency (%)
0.12982181
0.2%
1.16141511
0.2%
1.16667941
0.2%
1.70862581
0.2%
1.72039411
0.2%
1.80698011
0.2%
1.86869051
0.2%
1.87448121
0.2%
1.88282391
0.2%
1.9149591
0.2%
ValueCountFrequency (%)
27.64259341
0.2%
22.92961881
0.2%
19.3517381
0.2%
15.88843731
0.2%
15.57329181
0.2%
15.26377871
0.2%
15.2255631
0.2%
15.21374321
0.2%
14.83041381
0.2%
13.06326871
0.2%

ndm
Real number (ℝ)

HIGH CORRELATION

Distinct659
Distinct (%)100.0%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.5781846352
Minimum-0.4237051
Maximum1.2756419
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)0.5%
Memory size5.3 KiB
2022-02-24T14:57:38.206443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.4237051
5-th percentile0.21083968
Q10.45235085
median0.5768565
Q30.71413735
95-th percentile0.91586275
Maximum1.2756419
Range1.699347
Interquartile range (IQR)0.2617865

Descriptive statistics

Standard deviation0.2151740691
Coefficient of variation (CV)0.3721545956
Kurtosis1.016502936
Mean0.5781846352
Median Absolute Deviation (MAD)0.1320167
Skewness-0.1298760442
Sum381.0236746
Variance0.04629988001
MonotonicityNot monotonic
2022-02-24T14:57:38.304697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.46148961
 
0.2%
0.44336011
 
0.2%
0.82695171
 
0.2%
0.41490521
 
0.2%
0.39317821
 
0.2%
0.35153331
 
0.2%
0.71451331
 
0.2%
0.08407781
 
0.2%
0.82606011
 
0.2%
0.65029071
 
0.2%
Other values (649)649
98.3%
ValueCountFrequency (%)
-0.42370511
0.2%
-0.19224141
0.2%
-0.14581391
0.2%
0.01848051
0.2%
0.05492211
0.2%
0.07550331
0.2%
0.07761981
0.2%
0.08407781
0.2%
0.09248381
0.2%
0.09978771
0.2%
ValueCountFrequency (%)
1.27564191
0.2%
1.21902391
0.2%
1.19565141
0.2%
1.163171
0.2%
1.157321
0.2%
1.15300811
0.2%
1.13788211
0.2%
1.11523331
0.2%
1.11243431
0.2%
1.10865251
0.2%

ndfdm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct659
Distinct (%)100.0%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean69.90467393
Minimum56.4808807
Maximum84.7447891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-02-24T14:57:38.411413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum56.4808807
5-th percentile61.20755503
Q166.102417
median69.8025208
Q373.37451175
95-th percentile79.11199493
Maximum84.7447891
Range28.2639084
Interquartile range (IQR)7.27209475

Descriptive statistics

Standard deviation5.440097741
Coefficient of variation (CV)0.07782165963
Kurtosis-0.4694186041
Mean69.90467393
Median Absolute Deviation (MAD)3.6976624
Skewness0.07907337762
Sum46067.18012
Variance29.59466343
MonotonicityNot monotonic
2022-02-24T14:57:38.506160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.50079731
 
0.2%
71.79451751
 
0.2%
67.01379391
 
0.2%
72.42018891
 
0.2%
74.64208981
 
0.2%
74.96525571
 
0.2%
71.92496491
 
0.2%
67.75401311
 
0.2%
66.39045721
 
0.2%
72.74925991
 
0.2%
Other values (649)649
98.3%
ValueCountFrequency (%)
56.48088071
0.2%
57.28205111
0.2%
57.50375371
0.2%
57.7300721
0.2%
57.88604741
0.2%
58.07337951
0.2%
58.36970141
0.2%
58.67091751
0.2%
58.7706681
0.2%
58.97851941
0.2%
ValueCountFrequency (%)
84.74478911
0.2%
84.3204881
0.2%
83.13464361
0.2%
82.75202941
0.2%
82.46125031
0.2%
82.04534911
0.2%
82.00326541
0.2%
81.64050291
0.2%
81.64044951
0.2%
81.45138551
0.2%

adfdm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct659
Distinct (%)100.0%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean41.13477778
Minimum29.7261391
Maximum55.5958862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-02-24T14:57:38.608700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum29.7261391
5-th percentile33.65673824
Q137.17245105
median40.8011665
Q344.88758085
95-th percentile49.90926135
Maximum55.5958862
Range25.8697471
Interquartile range (IQR)7.7151298

Descriptive statistics

Standard deviation5.100505001
Coefficient of variation (CV)0.123994957
Kurtosis-0.6160972243
Mean41.13477778
Median Absolute Deviation (MAD)3.8091851
Skewness0.2398804732
Sum27107.81856
Variance26.01515126
MonotonicityNot monotonic
2022-02-24T14:57:38.712463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.34677121
 
0.2%
40.34371191
 
0.2%
38.33362581
 
0.2%
42.79919431
 
0.2%
43.96950531
 
0.2%
46.55028531
 
0.2%
41.19517521
 
0.2%
50.58267971
 
0.2%
37.57199861
 
0.2%
43.03243261
 
0.2%
Other values (649)649
98.3%
ValueCountFrequency (%)
29.72613911
0.2%
29.79671481
0.2%
30.10924911
0.2%
30.36914251
0.2%
30.83238981
0.2%
31.30270581
0.2%
31.33845141
0.2%
31.53559881
0.2%
31.60919761
0.2%
31.65521431
0.2%
ValueCountFrequency (%)
55.59588621
0.2%
54.53965761
0.2%
53.89516831
0.2%
53.86645511
0.2%
53.45098881
0.2%
53.31333921
0.2%
53.21606061
0.2%
53.00841521
0.2%
52.59884261
0.2%
52.331811
0.2%

adldm
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct659
Distinct (%)100.0%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5.115280357
Minimum-1.9228789
Maximum8.8102074
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)0.5%
Memory size5.3 KiB
2022-02-24T14:57:38.815689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1.9228789
5-th percentile2.91569283
Q14.4534354
median5.1689587
Q35.92003945
95-th percentile7.07993459
Maximum8.8102074
Range10.7330863
Interquartile range (IQR)1.46660405

Descriptive statistics

Standard deviation1.327256471
Coefficient of variation (CV)0.2594689593
Kurtosis2.992659162
Mean5.115280357
Median Absolute Deviation (MAD)0.7411966
Skewness-0.9287936424
Sum3370.969755
Variance1.761609739
MonotonicityNot monotonic
2022-02-24T14:57:38.917419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.07996511
 
0.2%
5.74507471
 
0.2%
5.46858791
 
0.2%
5.89939881
 
0.2%
6.07409481
 
0.2%
6.82577751
 
0.2%
4.74462181
 
0.2%
4.02907751
 
0.2%
4.93755441
 
0.2%
5.28641941
 
0.2%
Other values (649)649
98.3%
ValueCountFrequency (%)
-1.92287891
0.2%
-0.34855011
0.2%
-0.01993941
0.2%
0.0236231
0.2%
0.32892041
0.2%
0.35644651
0.2%
0.40370041
0.2%
0.69082461
0.2%
0.82301441
0.2%
0.92638911
0.2%
ValueCountFrequency (%)
8.81020741
0.2%
8.66526411
0.2%
8.32558921
0.2%
8.26564411
0.2%
8.24735831
0.2%
8.19294071
0.2%
7.97034981
0.2%
7.7602011
0.2%
7.71487571
0.2%
7.68181091
0.2%

me
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct659
Distinct (%)100.0%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean7.135643075
Minimum5.2690411
Maximum9.1221209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-02-24T14:57:39.017695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5.2690411
5-th percentile5.90551986
Q16.57671425
median7.166604
Q37.7202077
95-th percentile8.27531737
Maximum9.1221209
Range3.8530798
Interquartile range (IQR)1.14349345

Descriptive statistics

Standard deviation0.7392162998
Coefficient of variation (CV)0.1035949097
Kurtosis-0.798187178
Mean7.135643075
Median Absolute Deviation (MAD)0.5735573
Skewness-0.1206838827
Sum4702.388786
Variance0.5464407379
MonotonicityNot monotonic
2022-02-24T14:57:39.121822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.98012691
 
0.2%
6.89611241
 
0.2%
7.15991311
 
0.2%
6.99045231
 
0.2%
6.52389531
 
0.2%
6.27757411
 
0.2%
7.07115361
 
0.2%
5.80504851
 
0.2%
7.29885771
 
0.2%
6.66612721
 
0.2%
Other values (649)649
98.3%
ValueCountFrequency (%)
5.26904111
0.2%
5.34673741
0.2%
5.35300541
0.2%
5.45417691
0.2%
5.48090311
0.2%
5.62653731
0.2%
5.66732551
0.2%
5.67678361
0.2%
5.67994691
0.2%
5.68132451
0.2%
ValueCountFrequency (%)
9.12212091
0.2%
8.94698621
0.2%
8.59128671
0.2%
8.54044911
0.2%
8.53692251
0.2%
8.52865981
0.2%
8.52500341
0.2%
8.49196431
0.2%
8.47896291
0.2%
8.42890741
0.2%

ivomd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct659
Distinct (%)100.0%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean47.48231918
Minimum34.7050972
Maximum61.5963135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-02-24T14:57:39.226890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum34.7050972
5-th percentile39.38030931
Q143.39669035
median47.640419
Q351.22227475
95-th percentile55.00163121
Maximum61.5963135
Range26.8912163
Interquartile range (IQR)7.8255844

Descriptive statistics

Standard deviation4.946896738
Coefficient of variation (CV)0.1041839746
Kurtosis-0.7375340324
Mean47.48231918
Median Absolute Deviation (MAD)3.8179703
Skewness-0.05805723215
Sum31290.84834
Variance24.47178734
MonotonicityNot monotonic
2022-02-24T14:57:39.332291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.92201231
 
0.2%
45.19609831
 
0.2%
48.72601321
 
0.2%
46.02282331
 
0.2%
42.84916691
 
0.2%
41.67368321
 
0.2%
47.43985371
 
0.2%
40.67957311
 
0.2%
49.60396581
 
0.2%
44.77019881
 
0.2%
Other values (649)649
98.3%
ValueCountFrequency (%)
34.70509721
0.2%
35.32703781
0.2%
36.42728811
0.2%
37.15554051
0.2%
37.36883541
0.2%
37.43275451
0.2%
37.81784821
0.2%
37.82539371
0.2%
37.87291341
0.2%
37.89180371
0.2%
ValueCountFrequency (%)
61.59631351
0.2%
58.50537491
0.2%
58.2588731
0.2%
58.24099731
0.2%
58.07452391
0.2%
57.7792741
0.2%
57.76429751
0.2%
57.61140061
0.2%
57.49695971
0.2%
57.05997471
0.2%

GH > 3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct659
Distinct (%)100.0%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1.243352884
Minimum0.4319761
Maximum13.201643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-02-24T14:57:39.434051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.4319761
5-th percentile0.66176297
Q10.8689699
median1.0391817
Q31.3285534
95-th percentile2.41619775
Maximum13.201643
Range12.7696669
Interquartile range (IQR)0.4595835

Descriptive statistics

Standard deviation0.8707924251
Coefficient of variation (CV)0.7003582302
Kurtosis70.22709313
Mean1.243352884
Median Absolute Deviation (MAD)0.2018796
Skewness6.774800326
Sum819.3695503
Variance0.7582794476
MonotonicityNot monotonic
2022-02-24T14:57:39.536112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.67069691
 
0.2%
1.80826261
 
0.2%
0.79868561
 
0.2%
1.20789671
 
0.2%
1.13351061
 
0.2%
1.09489721
 
0.2%
0.58550941
 
0.2%
3.52326371
 
0.2%
0.74033941
 
0.2%
1.0664451
 
0.2%
Other values (649)649
98.3%
ValueCountFrequency (%)
0.43197611
0.2%
0.46025281
0.2%
0.47973221
0.2%
0.50970981
0.2%
0.53382321
0.2%
0.54034271
0.2%
0.54311641
0.2%
0.56101711
0.2%
0.56867021
0.2%
0.57319321
0.2%
ValueCountFrequency (%)
13.2016431
0.2%
8.27165321
0.2%
8.25317481
0.2%
7.05347541
0.2%
5.25701431
0.2%
4.63674311
0.2%
4.5391041
0.2%
4.32778741
0.2%
4.13219831
0.2%
3.90792941
0.2%

NH > 1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct659
Distinct (%)100.0%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.4700432472
Minimum0.1573858
Maximum2.661741
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-02-24T14:57:39.636033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1573858
5-th percentile0.24326204
Q10.3052868
median0.3748042
Q30.5087445
95-th percentile1.01502057
Maximum2.661741
Range2.5043552
Interquartile range (IQR)0.2034577

Descriptive statistics

Standard deviation0.293301281
Coefficient of variation (CV)0.6239878623
Kurtosis14.11304423
Mean0.4700432472
Median Absolute Deviation (MAD)0.0872552
Skewness3.115155449
Sum309.7584999
Variance0.08602564144
MonotonicityNot monotonic
2022-02-24T14:57:39.737901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.91224251
 
0.2%
0.50014711
 
0.2%
0.33610441
 
0.2%
0.34510411
 
0.2%
0.40773821
 
0.2%
0.44815861
 
0.2%
0.25590321
 
0.2%
0.78945991
 
0.2%
0.262221
 
0.2%
0.32798861
 
0.2%
Other values (649)649
98.3%
ValueCountFrequency (%)
0.15738581
0.2%
0.1622921
0.2%
0.1698751
0.2%
0.17287191
0.2%
0.1790961
0.2%
0.18375171
0.2%
0.18803841
0.2%
0.1999081
0.2%
0.20211051
0.2%
0.20480081
0.2%
ValueCountFrequency (%)
2.6617411
0.2%
2.57489851
0.2%
2.31242471
0.2%
2.03628831
0.2%
2.00933241
0.2%
1.64660231
0.2%
1.64065331
0.2%
1.55744561
0.2%
1.52682461
0.2%
1.52125451
0.2%

variety
Categorical

HIGH CARDINALITY

Distinct242
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Soubatimi
59 
Peke
 
36
Pablo
 
26
Jakunbe
 
18
Tiandougou coura/Kénikénidiéma
 
12
Other values (237)
509 

Length

Max length48
Median length13
Mean length18.93787879
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)3.2%

Sample

1st rowICSX 17660224-SB28-SS1-SS1-1
2nd rowICSX 17660224-SB28-SS1-SS1-1
3rd rowICSX 17660224-SB28-SS7-SS1-1
4th rowICSX 17660224-SB28-SS7-SS1-1
5th rowICSX 17660224-SB28-SS13-SS1-1

Common Values

ValueCountFrequency (%)
Soubatimi59
 
8.9%
Peke36
 
5.5%
Pablo26
 
3.9%
Jakunbe18
 
2.7%
Tiandougou coura/Kénikénidiéma12
 
1.8%
Fadda8
 
1.2%
Sassilon6
 
0.9%
ICSX 1765690:H5
 
0.8%
ICSX 17651145:H5
 
0.8%
Lata/Ridb-8-9-1-1-vrac5
 
0.8%
Other values (232)480
72.7%

Length

2022-02-24T14:57:39.839585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
icsx82
 
6.5%
0262
 
4.9%
soubatimi59
 
4.7%
icsb58
 
4.6%
tiandougou52
 
4.2%
icsv40
 
3.2%
63971940
 
3.2%
peke36
 
2.9%
samboni/irat32
 
2.6%
pablo26
 
2.1%
Other values (233)766
61.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-02-24T14:57:36.094478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:28.352325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:29.483586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:30.293419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:31.155113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:31.975019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:32.783854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:33.599325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:34.449072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:35.269496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:36.173010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:28.434118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:29.561378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:30.375200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:31.234899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:32.052811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:32.862644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:33.682104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:34.526510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:35.351274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:36.249836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:28.515887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:29.640167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:30.459973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:31.314403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:32.132598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:32.942430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:33.764882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:34.606268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:35.431061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:36.334578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:28.602655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:29.724940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:30.550730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:31.403166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:32.218369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:33.028203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:33.857668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:34.693035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:35.518826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:36.414239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:28.679738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:29.804727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:30.636501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:31.484947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:32.297157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:33.107987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:33.940413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:34.773819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:35.600607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:36.491033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:28.760523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:29.885510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:30.722271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:31.565731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:32.377942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:33.189769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:34.024188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:34.854603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:35.681410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:36.573812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:28.839311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:29.964300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:30.805049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:31.645901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:32.456741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:33.270669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:34.108961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:34.936384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:35.762174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:36.659582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:28.926105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:30.048075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:30.896804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:31.729676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:32.542533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:33.356607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:34.197723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:35.024149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:35.847944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:36.737374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:29.002873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:30.127862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:30.982574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:31.811478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:32.622288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:33.437391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:34.283495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:35.105931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:35.930908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:36.818158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:29.082659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:30.213643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:31.072334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:31.895234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:32.706063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:33.520536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:34.368267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:35.188709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-24T14:57:36.015670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-24T14:57:39.917380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-24T14:57:40.024126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-24T14:57:40.128845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-24T14:57:40.251161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-24T14:57:36.946845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-24T14:57:37.101589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-24T14:57:37.562931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-24T14:57:37.677593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

dmashndmndfdmadfdmadldmmeivomdGH > 3NH > 1variety
093.1015709.7031310.46149060.50079737.3467713.0799657.98012753.9220121.6706970.912242ICSX 17660224-SB28-SS1-SS1-1
191.9680866.2639050.69734863.47185537.2587934.9132267.60125351.1262280.6585840.248810ICSX 17660224-SB28-SS1-SS1-1
293.6263286.3729990.78663664.74695637.1530004.8538607.56823049.8338241.1240200.379298ICSX 17660224-SB28-SS7-SS1-1
392.9576654.7285190.35431466.79805038.5027244.7695117.78196950.9086491.0227210.305397ICSX 17660224-SB28-SS7-SS1-1
492.4181904.7652990.78337159.87128832.2225494.6161808.14161455.2100261.2438320.422767ICSX 17660224-SB28-SS13-SS1-1
592.3610696.6127830.78260561.42226035.6633454.9614447.55998052.5224530.8599880.363903ICSX 17660224-SB28-SS13-SS1-1
693.0878145.8314880.51610559.63824832.8512153.8435968.40969056.3816761.2579260.722176ICSX 17660224-SB28-SS14-SS1-1
792.1914376.0116610.75112562.41323534.6109703.8745258.17433054.6404720.7529270.157386ICSX 17660224-SB28-SS14-SS1-1
892.8686454.9588450.49193465.20446835.3221514.1239508.00065753.0206531.1339340.278522ICSX 17660227-SB6-SS6-SS1-1
994.0834358.0843510.26694372.27499448.1199576.2858936.39714842.4955712.5136760.931992ICSX 17660227-SB6-SS6-SS1-1

Last rows

dmashndmndfdmadfdmadldmmeivomdGH > 3NH > 1variety
65094.03373012.1136400.15276571.86822551.5550165.5592165.82930940.0591432.3920840.715506Pablo
65192.7578283.4803050.53190066.96022836.8689964.3737397.93502251.7301871.0035760.453451Jakunbe
65294.1866153.1644020.28380978.66169049.9340977.3608455.99337439.8989941.1521370.393013Soubatimi
65393.8702854.9041860.90005674.45930547.7854967.4242066.01524440.9382550.8908900.453942Pablo
65494.1250841.8686910.30615481.64050349.6123206.4470296.51710442.1684341.3226460.479139Jakunbe
65592.9259036.2176210.61024071.44988336.1000371.2769117.86673052.0235061.9857341.303614Pablo
65692.9287725.5851960.96244567.07256334.9441683.0050027.96771753.4226650.9647960.442202Soubatimi
65794.3656925.3095400.68657778.98613049.6713266.7992776.06693840.4810790.6573280.355861Soubatimi
65894.2949684.2404310.66528979.38490346.4848255.2365096.41302642.7895851.0605470.511866Jakunbe
659NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPablo